Course description
Control systems, communication and networking, electronic circuit design, data analysis and modelling, statistics, signal and image processing, and finance are only a few among numerous areas of modern engineering and science, which routinely depend on the methods of numerical optimization. Whether one is prototyping an electronic circuit or developing an investment portfolio, the desired solution can often be associated with an extremum of a specially designed function, known as a cost or objective function. Consequently, the task of finding an optimal solution can often be cast in the form of finding a point, at which such function reaches its minimum (or, alternatively, maximum) value. Naturally, how tractable and realizable the above task is depends on the properties of the cost function at hand as well as its domain and range, both of which can be either continuous or discrete. Accordingly, the fundamental objective of this course is to introduce some principal concepts of optimization theory along with its key numerical techniques. Throughout the course, students will gain valuable background in optimization methods applicable to a wide range of engineering problems along with experience in solving optimization problems of their own choice. The course outline in the PDF format can be found here.
Recommended reading
- S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004 (freely available online from here).
- D. P. Bertsekas, Network Optimization: Continuous and Discrete Models, Athena Scientific, 1998 (freely available online from here).